{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "8a2064e5-e1cd-4b8c-b8ca-8ecda9184d6c",
   "metadata": {},
   "outputs": [],
   "source": [
    "import struct\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import xarray as xr\n",
    "import matplotlib.pyplot as plt\n",
    "import fiona\n",
    "from matplotlib.path import Path\n",
    "import matplotlib as mpl\n",
    "import re\n",
    "%config InlineBackend.figure_format='retina'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "b728a4d6-722c-48b7-99c6-1ffbddc42d28",
   "metadata": {},
   "outputs": [],
   "source": [
    "###function to calculate distance between points\n",
    "\n",
    "def great_circle_dist_moon(p1,p2,r=1737.0):\n",
    "    \"\"\"\n",
    "    Calculate great-circle distance between two points\n",
    "    \n",
    "    Input:\n",
    "        p1 = [lon, lat] \n",
    "        p2 = [lon, lat]\n",
    "\n",
    "        r = Radius (Default 1737.0 km) \n",
    "    Output:\n",
    "        d = distance (Default km)\n",
    "    \"\"\"\n",
    "    \n",
    "    lon1 = np.deg2rad(p1[0])\n",
    "    lon2 = np.deg2rad(p2[0])\n",
    "    lat1 = np.deg2rad(p1[1])\n",
    "    lat2 = np.deg2rad(p2[1])\n",
    "    \n",
    "    cent_ang = np.arccos(np.sin(lat1)*np.sin(lat2) + np.cos(lat1)*np.cos(lat2)*np.cos(np.abs(lon1-lon2)))\n",
    "    d = r*cent_ang\n",
    "    return d"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "05e7bbb8-cefa-4948-834e-8c7fd280f5d3",
   "metadata": {},
   "source": [
    "# Load Datasets:\n",
    "\n",
    "### Thorium Data\n",
    "https://pds-geosciences.wustl.edu/missions/lunarp/reduced/thoriumhd.txt "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "532386d4-f239-4bb4-95a6-cd53abd63c6a",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/zj/46zkt8ns517_676qkc7f89tm0000gv/T/ipykernel_58078/2391662833.py:2: ParserWarning: Falling back to the 'python' engine because the 'c' engine does not support regex separators (separators > 1 char and different from '\\s+' are interpreted as regex); you can avoid this warning by specifying engine='python'.\n",
      "  dat_th = pd.read_csv('../data/thoriumhd.txt', skiprows = 13, sep=',\\s+', lineterminator='\\n', names=[\"lat_min\",\"lat_max\",\"lon_min\",\"lon_max\",\"ppm\"])\n"
     ]
    }
   ],
   "source": [
    "## Load thorium data\n",
    "dat_th = pd.read_csv('../data/thoriumhd.txt', skiprows = 13, sep=',\\s+', lineterminator='\\n', names=[\"lat_min\",\"lat_max\",\"lon_min\",\"lon_max\",\"ppm\"])\n",
    "\n",
    "ppm = dat_th.ppm.values.reshape([360,720])\n",
    "thlon = dat_th.lon_min.values.reshape([360,720])\n",
    "thlat = dat_th.lat_min.values.reshape([360,720])\n",
    "\n",
    "\n",
    "## transform into xarray Dataset\n",
    "thor = xr.Dataset(dict(th_ppm = ([\"x\",\"y\"],ppm)),coords = dict(lat=([\"x\",\"y\"],thlat),lon=([\"x\",\"y\"],thlon))) \n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a0e1d27e-0f60-4aae-8743-ca64d22a3adb",
   "metadata": {
    "tags": []
   },
   "source": [
    "### LROC WAC Mosaic\n",
    "\n",
    "Note: using PDS3 version but recently has been updated to PDS4. Have not yet figure out how to display .TIF file.\n",
    "\n",
    "https://pds.lroc.asu.edu/data/LRO-L-LROC-5-RDR-V1.0/LROLRC_2001/EXTRAS/BROWSE/WAC_GLOBAL/WAC_GLOBAL_E000N0000_016P.TIF\n",
    "\n",
    "First, remove header lines and save to new file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "f4d0b10d-d230-4b06-9e6d-8898273bd357",
   "metadata": {},
   "outputs": [],
   "source": [
    "#load higher Res\n",
    "\n",
    "# Load WAC map lower res\n",
    "\n",
    "wacfile_bigger = '/work1/moon/WAC/WAC_GLOBAL_E000N0000_016P_nohead.txt'\n",
    "\n",
    "LINES = 2880 #rows / lat\n",
    "LINE_SAMPLES = 5760 #collumns /lon\n",
    "SAMPLE_BITS = 32\n",
    "BYTES_PER_PIXEL = int(SAMPLE_BITS/8)\n",
    "\n",
    "llat_big = np.linspace(-90,90,LINES)\n",
    "llon_big = np.linspace(-180,180,LINE_SAMPLES)\n",
    "LLON_big, LLAT_big = np.meshgrid(llon_big,llat_big)\n",
    "\n",
    "pixels = LINES*LINE_SAMPLES\n",
    "fmt = '{endian}{pixels}{fmt}'.format(endian='<',pixels=pixels,fmt='f')\n",
    "#plt.figure(figsize = [20,16])\n",
    "with open(wacfile_bigger,'rb') as f:\n",
    "    wacimg_big = np.array(struct.unpack(fmt,f.read(pixels*BYTES_PER_PIXEL))).reshape(LINES,LINE_SAMPLES)\n",
    "wacimg_big_corr = np.flip(np.flip(wacimg_big), axis=1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "5643958a-7bcd-4112-af48-f033f8c98af6",
   "metadata": {},
   "outputs": [],
   "source": [
    "th_mask_180 = np.loadtxt(\"../results/mare_mask_0.5deg.txt\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8767dde2-53d2-434d-8e60-9a889234474f",
   "metadata": {},
   "source": [
    "### Apply Mare Mask to Thorium Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "f319bf2d-e8fb-4bef-b325-94d8707eab64",
   "metadata": {},
   "outputs": [],
   "source": [
    "thor_nomare = thor.where(th_mask_180 == False)\n",
    "weight = np.cos(np.deg2rad(np.abs(thor.lat)))\n",
    "\n",
    "\n",
    "thor_nomare['weights'] = (('x','y'),weight.values)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c1b73962-fb51-427e-bd6f-d771e28a3e95",
   "metadata": {},
   "source": [
    "### TSTA Results File:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "6196d068-6fa5-4ae5-9c13-993b4c61f71c",
   "metadata": {},
   "outputs": [],
   "source": [
    "full_craterdat = pd.read_csv('../results/tsta_20_200.csv')\n",
    "full_craterdat = full_craterdat.set_index(full_craterdat.idx)\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "db478aa2-b312-4f46-bc06-87e61b86d1c2",
   "metadata": {
    "tags": []
   },
   "source": [
    "# Constants and Functions\n",
    "\n",
    "### LP GRS Instrument Response Function \n",
    "(Lawrence et al. 2003, Kivelson 1995)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "270402e0-d3c5-4535-b1d4-ffa0071712e9",
   "metadata": {},
   "outputs": [],
   "source": [
    "A = 1; \n",
    "\n",
    "sigma = lambda h: 0.704*h + 1.39\n",
    "kappa = lambda h: -4.87*(10**(-4))*h + 0.631\n",
    "\n",
    "w = lambda D, h: A*(1 + D**2/(2*sigma(h)**2))**(-kappa(h)-1)\n",
    "\n",
    "D = np.linspace(-600,600,100) #Distance from sub-nadir point (km)\n",
    "h = 30 #Altitude (km) \n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b227149a-54c3-4a07-9a92-1c9bb65441ed",
   "metadata": {},
   "source": [
    "# Figure 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "c78f2702-997d-49a7-9cd9-1050efd5e07d",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "full_craterdat['diff'] = full_craterdat['ejecta_m_t'] - full_craterdat['floor_m_t'] \n",
    "\n",
    "testcr_blu = full_craterdat.loc['02-1-001080']\n",
    "\n",
    "lon_perimeter = np.array(re.split('\\s+|\\n',testcr_blu.lon_perimeter.replace(']','').replace('[','').strip())).astype(float)\n",
    "lat_perimeter = np.array(re.split('\\s+|\\n',testcr_blu.lat_perimeter.replace(']','').replace('[','').strip())).astype(float)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "41f01ced-ad6f-45bd-ae8f-7717b1d59c18",
   "metadata": {},
   "outputs": [],
   "source": [
    "### repeat TSTA analysis to plot\n",
    "D = np.linspace(-600,600,100) \n",
    "h = 30\n",
    "\n",
    "dc = testcr_blu.rc*2\n",
    "lon = testcr_blu.lon\n",
    "lat = testcr_blu.lat\n",
    "rc = testcr_blu.rc\n",
    "\n",
    "if lon > 180.0:\n",
    "        lon_th = lon - 360.0\n",
    "else:\n",
    "    lon_th = lon\n",
    "\n",
    "    \n",
    "\n",
    "r_ej = 2*rc #ejecta ~1 crater radius away from the rim\n",
    "r_back = r_ej + 300\n",
    "bins = np.linspace(0,r_back,100) #radius of crater\n",
    "\n",
    "#masks for each region and data averages\n",
    "gc_dist = great_circle_dist_moon([thor.lon.values,thor.lat.values], [lon,lat])\n",
    "\n",
    "\n",
    "cratermaskarr = np.zeros_like(gc_dist)\n",
    "cratermaskarr[np.where(gc_dist < rc)] = 1 #create floor mask\n",
    "\n",
    "\n",
    "\n",
    "ejectamaskarr = np.zeros_like(gc_dist)\n",
    "ejectamaskarr[np.where((gc_dist < r_ej ) & (gc_dist > rc))] = 1 #create annulus mask\n",
    "\n",
    "backmaskarr = np.zeros_like(gc_dist)\n",
    "backmaskarr[np.where((gc_dist < r_back) & (gc_dist > r_ej))] = 1 #create annulus mask\n",
    "\n",
    "weight = np.cos(np.deg2rad(np.abs(thor.lat)))\n",
    "\n",
    "\n",
    "crat_th_m = thor_nomare.th_ppm.where(cratermaskarr == 1).weighted(weight).mean()\n",
    "crat_th_std = thor_nomare.th_ppm.where(cratermaskarr == 1).std()\n",
    "\n",
    "ej_th_m = thor_nomare.th_ppm.where(ejectamaskarr == 1).weighted(weight).mean()\n",
    "ej_th_std = thor_nomare.th_ppm.where(ejectamaskarr == 1).std()\n",
    "\n",
    "back_m = thor_nomare.th_ppm.where(backmaskarr == 1).weighted(weight).mean()\n",
    "\n",
    "\n",
    "    ##weighted standard deviation \n",
    "M_crater = np.count_nonzero(cratermaskarr)\n",
    "\n",
    "thor_nomare['weights'] = (('x','y'),weight.values)\n",
    "\n",
    "if M_crater > 1:\n",
    "    std_w_crater = np.sqrt((weight*(thor_nomare.th_ppm.where(cratermaskarr == 1) - crat_th_m.item())**2).sum()/\n",
    "                    (((M_crater-1)/M_crater)*weight.where(cratermaskarr==1).sum())).item()\n",
    "else:\n",
    "    std_w_crater = np.nan\n",
    "\n",
    "M_ej = np.count_nonzero(ejectamaskarr)\n",
    "if M_ej > 1:\n",
    "    std_w_ej = np.sqrt((weight*(thor_nomare.th_ppm.where(ejectamaskarr == 1) - ej_th_m.item())**2).sum()/\n",
    "                    (((M_ej-1)/M_ej)*weight.where(ejectamaskarr==1).sum())).item()\n",
    "else:\n",
    "    std_w_ej = np.nan\n",
    "M_back = np.count_nonzero(backmaskarr)\n",
    "\n",
    "if M_back > 1:\n",
    "    std_w_back = np.sqrt((weight*(thor_nomare.th_ppm.where(backmaskarr == 1) - back_m.item())**2).sum()/\n",
    "                    (((M_back-1)/M_back)*weight.where(backmaskarr==1).sum())).item()\n",
    "else:\n",
    "    std_w_back = np.nan\n",
    "\n",
    "\n",
    "\n",
    "relevant = np.zeros_like(gc_dist)\n",
    "relevant[np.where(gc_dist< r_back)] = 1.0\n",
    "\n",
    "##radial profile\n",
    "distarr = gc_dist * relevant\n",
    "thor_nomare['distarr'] = (('x','y'),distarr) #distance up to background border\n",
    "\n",
    "gb_all = thor_nomare.groupby_bins(thor_nomare.distarr,bins[1:]) #separate into bins by distance\n",
    "\n",
    "ngroups = len(gb_all.groups)\n",
    "\n",
    "prof = np.zeros((ngroups,2))\n",
    "\n",
    "i = 0\n",
    "#weighted average of each bin\n",
    "\n",
    "for gr in gb_all.groups:\n",
    "    keep = ~np.isnan(gb_all[gr].th_ppm.values) # non nan values\n",
    "    if keep.any() == True:\n",
    "        prof[i,:] = [gr.left,np.average(gb_all[gr].th_ppm.values[keep],weights = gb_all[gr].weights.values[keep])]\n",
    "    else:\n",
    "        prof[i,:] = [gr.left,np.nan]\n",
    "    #print(gr, np.average(gb_all[gr].th_ppm.values,weights = gb_all[gr].weights.values))\n",
    "    i = i + 1\n",
    "sort_prof = prof[prof[:, 0].argsort()]\n",
    "\n",
    "doubled = np.append(np.flip(sort_prof[:,1]),sort_prof[:,1])\n",
    "\n",
    "D_prof = np.append((-1)*np.flip(sort_prof[:,0]),sort_prof[:,0])\n",
    "\n",
    "\n",
    "\n",
    "#D_prof = np.append((-1)*np.flip(bins[1:-1]),bins[1:-1])\n",
    "double_mask = doubled.copy()\n",
    "double_mask[np.where(np.abs(D_prof) > r_back)] = 0 #mask out irrelevant areas\n",
    "\n",
    "\n",
    "convdat = np.convolve(double_mask, w(D,h))\n",
    "\n",
    "D_conv = np.linspace(-600,600,len(convdat))\n",
    "\n",
    "###set up forward model\n",
    "\n",
    "#floor model\n",
    "modc = np.zeros_like(D_prof)\n",
    "modc[np.where(np.abs(D_prof) < rc)] = 1\n",
    "\n",
    "convc = np.convolve(modc,w(D,h))\n",
    "\n",
    "\n",
    "\n",
    "#ejectan model\n",
    "mode = np.zeros_like(D_prof)\n",
    "mode[np.where((np.abs(D_prof) > rc)&(np.abs(D_prof) < r_ej))] = 1\n",
    "\n",
    "conve = np.convolve(mode,w(D,h))\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "#background model\n",
    "modx = np.zeros_like(D_prof)\n",
    "modx[np.where((np.abs(D_prof) > r_ej)&(np.abs(D_prof) < r_back))] = 1\n",
    "\n",
    "convx = np.convolve(modx,w(D,h))\n",
    "### Simulated observations \n",
    "\n",
    "\n",
    "O3 = np.zeros((3,1))\n",
    "O3[0] = np.mean(convdat[np.where(np.abs(D_conv)  < rc)])\n",
    "O3[1] = np.mean(convdat[np.where((np.abs(D_conv)  < r_ej) & (np.abs(D_conv) > rc))])\n",
    "O3[2] = np.mean(convdat[np.where((np.abs(D_conv) > r_ej)&(np.abs(D_conv) < r_back))])\n",
    "\n",
    "## Instrument Response Matrix \n",
    "\n",
    "C3 = np.zeros((3,3))\n",
    "C3[0,0] = np.mean(convc[np.where(np.abs(D_conv)  < rc)]) #cii\n",
    "C3[0,1] = np.mean(conve[np.where(np.abs(D_conv)  < rc)]) #cie\n",
    "C3[0,2] = np.mean(convx[np.where(np.abs(D_conv)  < rc)]) #cix\n",
    "\n",
    "C3[1,0] = np.mean(convc[np.where((np.abs(D_conv)  < r_ej) & (np.abs(D_conv) > rc))]) #cei\n",
    "C3[1,1] = np.mean(conve[np.where((np.abs(D_conv)  < r_ej) & (np.abs(D_conv) > rc))]) #cee\n",
    "C3[1,2] = np.mean(convx[np.where((np.abs(D_conv)  < r_ej) & (np.abs(D_conv) > rc))]) #cex\n",
    "\n",
    "C3[2,0] = np.mean(convc[np.where((np.abs(D_conv) > r_ej)&(np.abs(D_conv) < r_back))]) #cxi\n",
    "C3[2,1] = np.mean(conve[np.where((np.abs(D_conv) > r_ej)&(np.abs(D_conv) < r_back))]) #cxe\n",
    "C3[2,2] = np.mean(convx[np.where((np.abs(D_conv) > r_ej)&(np.abs(D_conv) < r_back))]) #cxx\n",
    "\n",
    "#deconvolve\n",
    "try:\n",
    "    T3 = np.matmul(np.linalg.inv(C3),O3)\n",
    "except np.linalg.LinAlgError as err:\n",
    "    print('Singular matrix')\n",
    "    T3 = [np.nan,np.nan,np.nan]\n",
    "    \n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "11bf5978-04f9-49fb-87e0-bea4a9abb7c2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x600 with 4 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 536,
       "width": 993
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize = [12,6])\n",
    "\n",
    "gr = fig.add_gridspec(6,14)\n",
    "\n",
    "ax1 = fig.add_subplot(gr[0:2,:8])\n",
    "ax2 = fig.add_subplot(gr[2:,:8])\n",
    "ax3 = fig.add_subplot(gr[1:,8:])\n",
    "\n",
    "\n",
    "D_hi = np.linspace(0,600,500)\n",
    "ax1.plot(D_hi,w(D_hi,h),'k-',linewidth = 2, label = 'Instrument \\nResponse \\nFunction')\n",
    "#ax[0].set_xlabel('Distance from Center (km)', fontsize = 15)\n",
    "ax1.set_ylabel('Response',fontsize = 15)\n",
    "#ax[0].legend(loc='upper right', fontsize = 15)\n",
    "ax1.set_xlim([0,400])\n",
    "#ax1.set_yticklabels([0.0,1.0])\n",
    "ax1.tick_params(axis='x', which='major',top=False,labelbottom=False) \n",
    "ax1.legend(loc = 'upper right', fontsize = 12)\n",
    "\n",
    "\n",
    "ax2.plot([0,0],[1.4,2.4],color = 'darkblue', linestyle = '-',linewidth = 1.5, alpha = 0.4)\n",
    "\n",
    "ax2.plot([rc,rc],[0.0,3.0],color = 'darkblue', linestyle = '--', linewidth = 1.5,alpha = 0.4)\n",
    "\n",
    "ax2.plot([r_ej,r_ej],[0.0,3.0],color = 'darkblue', linestyle = '--', linewidth = 1.5,alpha = 0.4)\n",
    "ax2.plot(D_prof,doubled, 'k', alpha = 0.5, label ='GRS Data')\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "ax2.plot(D_prof,crat_th_m.values*modc + ej_th_m.values*mode + back_m.values*modx, 'r',label = 'Regional Average', lw = 2)\n",
    "ax2.plot(D_prof,modc*T3[0] + mode*T3[1] + modx*T3[2], 'b',label = 'Regional TSTA', lw = 2)\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "ax2.tick_params(axis='both', which='major', labelsize=14)\n",
    "\n",
    "\n",
    "ax2.legend(loc = 'upper right', fontsize = 12)\n",
    "ax2.set_xlabel('Distance from Center (km)', fontsize = 15)\n",
    "ax2.set_ylabel('Th (ppm)', fontsize = 15)\n",
    "ax2.set_xlim([0,350])\n",
    "ax2.set_ylim([0.0,2.0])\n",
    "\n",
    "\n",
    "ax3.set_aspect('equal', adjustable='box')\n",
    "\n",
    "\n",
    "c1 = ax3.pcolormesh(thor.lon,thor.lat,thor_nomare.th_ppm, vmin = 0.63, vmax = 2)\n",
    "cbar = plt.colorbar(c1, ax = ax3,shrink = 0.7, orientation = 'horizontal',extend = 'both')\n",
    "cbar.set_label(label = 'Th (ppm)',fontsize = 15)\n",
    "cbar.ax.tick_params(labelsize=14)\n",
    "\n",
    "#ax3.plot(testcr_blu.lon,testcr_blu.lat,'kx')\n",
    "ax3.set_xlim([testcr_blu.lon - 10, testcr_blu.lon + 10])\n",
    "ax3.set_ylim([testcr_blu.lat - 10, testcr_blu.lat + 10])\n",
    "ax3.plot(lon_perimeter, lat_perimeter, 'r.',markersize = 1)\n",
    "\n",
    "ax3.xaxis.tick_top()\n",
    "ax3.yaxis.tick_right()\n",
    "fig.subplots_adjust(wspace = 0.1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "be31eb70-1bef-437a-800a-21e7d5e9dd51",
   "metadata": {},
   "outputs": [],
   "source": [
    "fig.savefig('../../kreep_paper_figures/figure2.png',dpi = 200)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "29a450c0-7784-402b-abeb-b203808bedb2",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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